With the advancement of technology, more and more media content has been migrated from analog to digital format. The convergence of networks, devices, and services combined with the technological advancements in digital storage, multimedia compression, and miniaturization of digital cameras has led to an explosive growth of online video content. In addition to the professionally produced video content, user-generated content and content produced by hardcore amateurs are also on the rise. Videos can easily be shared over the Internet using popular video sharing sites such as You Tube® and Yahoo!® Video. Although the user experience is enhanced with the new means of content production, distribution and monetization, it has made illegal reproduction and distribution of digital content easier. Piracy of digital media content is increasing day by day and is a major cause of worry for the digital content owners.
A video is a sequence of scenes and a scene is a sequence of images called frames. Increasing volumes of online digital video content and large amount of information contained within each video make it a challenge to search and retrieve relevant video files from a large collection. Video data management systems aim at reducing this complexity by indexing the video files. Indexing of video content as well as many digital watermarking algorithms require the video to be split into scenes. Scene change detection (hereinafter may be referred to as ‘SCD’) is used for segmentation of videos into contiguous scenes. SCD is instantly performed by human but vast computational resources and efficient complex algorithms are required to automate this process. Scene change detection in videos is a primary requirement of video processing applications used for the purpose of generating data needed by video data management systems. Scene change detection is a fundamental step in content based video retrieval systems, video annotation systems, video indexing methods and video data management systems. Scene changes in videos can either be gradual or abrupt. Abrupt scene changes can result from editing cuts. Gradual scene changes result from spatial effects such as zoom, camera pan and tilt, dissolve, fade in, fade out or the like. Detection of scene changes effectively depends on finding the similarity or the difference between adjacent frames. SCD usually involves measurement of some differences between successive frames. There are several ways to detect the scenes in a video. Pixel wise difference and histogram based difference are some of the techniques used to measure the inter-frame difference.
The existing scene change detection algorithms can be classified into two groups. One group is compressed domain which consists of algorithms that operate on compressed data and other group is uncompressed domain or pixel domain which consists of algorithms that operate on pixel data. The algorithms in compressed domain operate on compressed data, like algorithms based on Macro blocks in MPEG compressed video, algorithms based on motion characterization and segmentation for detecting scene changes in MPEG compressed video, algorithms based on statistical sequential analysis on compressed bit streams, algorithms based on feature extraction based on motion information and vectors or edges or luminance information. The algorithms in uncompressed domain or pixel domain operate on pixel data directly like algorithms based on color diagrams, algorithms based on color histogram and fuzzy color histogram, algorithms based on edge detection and edge difference examinations, algorithms based on background difference and tracking and object tracking. Efficient segmentation of videos into scenes enables effective management of videos. Also, segmentation of video into scenes can lead to effective watermark embedding. Generally, same watermark is embedded inside the video stream which makes it difficult to maintain the statistical and perceptual invisibility. Embedding a different watermark in different scenes can help in achieving statistical and perceptual invisibility and also makes it difficult for the attacker to extract the watermark.
A number of video watermarking algorithms are proposed by the researchers. These algorithms can be classified into two domains; spatial domain or pixel domain video watermarking and frequency domain or transform domain video watermarking. In spatial domain video watermarking, the watermark is embedded in the video frames by simple addition or bit replacement of selected pixels. These methods are computationally fast but less robust. In frequency domain video watermarking methods, the video frame is transformed and watermark is embedded in the transform coefficients. These methods are robust to common signal processing attacks like compression but require high computational time.
The existing technologies have various limitations. They do not identify the scene change with high precision and recall. The efficiency is low because of high false positive rate and false negative rate. Many algorithms are sensitive to motion of object and camera, like zooming and panning. Luminance variance results in scenes to be incorrectly segmented like in cases of excessive brightness change or flickering. Some algorithms fail in case of scene change involving frames of high motion. Algorithms do not consistently perform in cases like a fade, a dissolve or a wipe.
The existing processes have limitations such as video watermarking based methods are unable to carry large amount of information such as a string containing owner's name, responsible person's name and transaction date reliably and existing video watermarking methods embed same watermark for all the instances of video. Further, existing watermarking methods are not suitable for real time applications as they require high watermark embedding time. Most of the video watermarking algorithms do not embed watermark in real-time and hence, not suitable for real-time applications like on-the-fly video watermarking. This is due to the fact that the watermark embedding is done sequentially.